Faculty Summaries
Brian L. Egleston
Brian L Egleston, PhD
Assistant Research Professor
Office Phone: 215-214-3917
Fax: 215-728-2553
Office: R383
  • 1. Tutorial on principal stratification-based sensitivity analysis: Application to smoking cessation studies
    In collaboration with K.L. Cropsey, Amy Lazev & Carolyn Heckman

    One problem with assessing effects of smoking cessation interventions on withdrawal symptoms is that symptoms are affected by whether participants abstain from smoking during trials. Those who enter a randomized trial but do not change smoking behavior might not experience withdrawal-related symptoms. In Egleston et al. (Clinical Trials, 2010), we presented a tutorial of how one can use a principal stratification sensitivity analysis to account for abstinence in the estimation of smoking cessation intervention effects. The article is intended to introduce researchers to principal stratification and describe how they might implement the methods. We motivated the problem by presenting a hypothetical example that demonstrated why estimating effects within observed abstention groups is problematic. We demonstrated how estimation of effects within groups defined by potential abstention that an individual would have in either arm of a study can provide meaningful inferences. We further described a sensitivity analysis method to estimate such effects, and used it to investigate effects of a combined behavioral and nicotine replacement therapy intervention on withdrawal symptoms in a female prisoner population.

    As presented in the paper, the intervention was found to reduce withdrawal symptoms but the effect was not statistically significant in the group that was observed to abstain. More importantly, the intervention was found to be highly effective in the group that would abstain regardless of intervention assignment. The effectiveness of the intervention in other potential abstinence strata depends on the sensitivity analysis assumptions. This work showed how a principal stratification sensitivity analysis can provide a meaningful method of accounting for abstinence effects in the evaluation of smoking cessation interventions on withdrawal symptoms.  Smoking researchers have previously recommended analyses in subgroups defined by observed abstention status in the evaluation of smoking cessation interventions. We believe that principal stratification analyses should replace such analyses as the preferred means of accounting for post-randomization abstinence effects in the evaluation of smoking cessation programs.

  • 2. Validity of estimating non-SHBG bound testosterone and estradiol from total hormone measurements in boys and girls
    In collaboration with D.W. Chandler

    Circulating levels of bioavailable estradiol and testosterone are often desirable for clinical practice or investigational studies of children.  However, assays to measure circulating hormone levels might not always be accessible. In Egleston et al. (Annals of Clinical Biochemistry, 2010), we sought to validate the empirical calculation of circulating bioavailable testosterone and estradiol in children.  Data came from 663 eight- to ten-year olds who were recruited to the Dietary Intervention Study in Children (DISC). DISC was a randomized clinical trial designed to test efficacy of a dietary intervention to reduce serum cholesterol (LDL-C) in children with elevated cholesterol.  Assay measures of estradiol, testosterone, sex hormone-binding globulin concentration (SHBG) and albumin concentration in girls as well as dihydrotestosterone in boys were measured for up to 10 years.  We calculated measures of circulating non-SHBG bound estradiol and testosterone from total hormone levels using the law of mass action. We compared proportional differences in assay measured minus calculated non-SHBG bound hormone levels versus their averages using generalized estimating equations-estimated linear regressions. On average, calculated values overestimated assay measured values (-11.7% for non-SHBG bound estradiol in girls and -2.6% for non-SHBG bound testosterone in boys). While calculated values might be useful for research purposes, they are generally not close enough for clinical purposes.

  • 3. Sensitivity analysis to investigate the impact of a missing covariate on survival analyses using cancer registry data
    In collaboration with Yu-Ning Wong

    Having substantial missing data is a common problem in administrative and cancer registry data. In Egleston and Wong (Statistics in Medicine, 2009), we proposed a sensitivity analysis to evaluate the impact of a covariate that is potentially missing not at random in survival analyses using Weibull proportional hazards regressions. We applied the method to an investigation of the impact of missing grade on post-surgical mortality outcomes in individuals with metastatic kidney cancer. Data came from the Surveillance Epidemiology and End Results (SEER) registry which provides population-based information on those undergoing cytoreductive nephrectomy. Tumor grade was an important component of risk stratification for patients with both localized and metastatic kidney cancer. Many individuals in SEER with metastatic kidney cancer were missing tumor grade information. We found that surgery was protective, but that the magnitude of the effect depended on assumptions about the relationship of grade with missingness.